Smoke videos used in "Smoke detection in video sequences based on dynamic texture using volume local binary patterns": train_set consisted of 10 smoke videos and 5 non-smoke videos,
test_set contained another 10 smoke videos that are similar to the train_set and the same 5 non-smoke videos in the train_set.

Smoke videos used in "Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images": The test_set contains 500 smoke images(testsmoke) and 500 non-smoke images(testnon);
The train_synthetic_smoke_set are produced by Blender-python.The train_real_smoke_set are captured by ourselves. The background images and non-smoke images are collected from ImageNet. We render each frame of smoke image with a new background image. The parameters of rendering, lighting and wind are set randomly in a certain range for diversity. We give a reference - smoke.blend for parameter sets of smoke rendering. As differnet sets of the parameters influence directly the appearance of synthetic smoke images, these images will be realistic or non-realistic.
Experiment showed that the non-realistic synthetic smoke images works just as well as more realistic synthetic smoke images.

The dataset used in "Smoke Detection Based on Scene Parsing and Saliency Segmentation": The The dataset for wildfire smoke detection contains 4695 images, which consists of 2695 images for training and 2000 images for test. Our dataset is mainly for the wild scene, composed from the video shot through video surveilance cameras in lookout towers and unmanned aerial vehicle (UAV).